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arXiv · 1907.03247

Resource-Efficient Computing in Wearable Systems

Mahdi Pedram·Mahsan Rofouei·Francesco Fraternali·Zhila Esna Ashari·Hassan Ghasemzadeh
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Abstract

We propose two optimization techniques to minimize memory usage and computation while meeting system timing constraints for real-time classification in wearable systems. Our method derives a hierarchical classifier structure for Support Vector Machine (SVM) in order to reduce the amount of computations, based on the probability distribution of output classes occurrences. Also, we propose a memory optimization technique based on SVM parameters, which results in storing fewer support vectors and as a result requiring less memory. To demonstrate the efficiency of our proposed techniques, we performed an activity recognition experiment and were able to save up to 35% and 56% in memory storage when classifying 14 and 6 different activities, respectively. In addition, we demonstrated that there is a trade-off between accuracy of classification and memory savings, which can be controlled based on application requirements.

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Mahdi Pedram, Mahsan Rofouei, Francesco Fraternali, Zhila Esna Ashari, Hassan Ghasemzadeh. 2019-07-07. Resource-Efficient Computing in Wearable Systems. https://arxiv.org/abs/1907.03247

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